Are Rising Tide students making progress toward our achievement accountability goal: “80% of Rising Students will test as at or above Benchmark in Reading on the Renaissance Star Reading and Star Mathematics screening assessments.?
Bench_Sum_Plot<-HistSchoolBenchSum%>%filter(`Year`=="2024-2025"&`Screening Window`=="Winter"|`Year`=="2023-2024"&`Screening Window`=="Spring")%>%filter(`Benchmark_Status`=="At/Above Benchmark")%>%ggplot(aes(x=`Benchmark_Status`,fill =`Year`, y =`% Students`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`% Students`, label =`% Students`,vjust =-.25), position =position_dodge(width =1)) +#theme_classic() +theme(axis.title.x=element_blank(),# axis.text.x = element_text(angle = 45, , hjust = 1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+labs(y ="% Students",x="Benchmark Category",title ="Rising Tide Benchmark Achievement by School Year",caption ="Source: Renaissance Star Literacy and Math Screening")+geom_hline(yintercept =80, color ="grey")+annotate("text", x = .75, y =85, label ="80% Goal") +facet_wrap(~Subject)
Code
#| eval: false#| fig-cap: "Rising Tide Benchmark Achievement by School Year, Renaissance Star Literacy and Math"#| fig-alt: >#| A bar blot with school year (ordered, factor) on the x-axis and the percentage of students#| classified as At/Above benchmark on the y-axis, faceted by subject (English Language Arts or Math)#| Plot illustrates the relationship between the three variables.Bench_Sum_Plot
Subject Level Fall Winter Growth Comparison
ELA Growth
Code
Growth_Sum_ELA<-star_dem_all_private%>%filter(`School_Year`=="2024-2025")%>%filter(`Assignment Type`=="Star Reading")%>%group_by(Grade)%>%summarize(median_SGP=median(`SGP (Expectation=50)`))Growth_Sum_ELA_Plot<-ggplot(Growth_Sum_ELA, aes(x=`Grade`, y =`median_SGP`,fill="skyblue")) +geom_bar(position="dodge", stat ="identity", fill="skyblue") +#facet_wrap(~Grade)+geom_text(aes( y =`median_SGP`, label =`median_SGP`,vjust =-.25))+theme_classic()+#scale_color_brewer(palette = "Blues")+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="Grade Level",title ="Literacy Student Growth Percentile by Grade Level",caption ="Source: 2024-2025 Renaissance Star Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =1., y =52, label ="50th Percentile")
Code
Growth_Sum_ELA_Plot
ELA Historical Growth
Code
Hist_Growth_Sum_ELA_Plot<-HistGradeGrowthSum%>%filter(Subject =="ELA")%>%filter(`Screening Window`=="Fall-Winter")%>%ggplot(aes(x=`Grade`,fill ="skyblue", y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity", fill ="skyblue") +#facet_wrap(~Grade)+geom_text(aes( y =`Median SGP`, label =`Median SGP`,vjust =-.25), position =position_dodge(width =1))+#theme_classic()+#scale_fill_brewer(palette = "Blues")+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="Grade Level",title ="Literacy Student Growth Percentile by Grade Level",caption ="Source: Renaissance Star Literacy Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =1.55, y =52, label ="50th Percentile")+facet_wrap(~Year)
Code
Hist_Growth_Sum_ELA_Plot
Math Growth
Code
Growth_Sum_Math<-star_dem_all_private%>%filter(`Assignment Type`=="Star Math")%>%filter(`School_Year`=="2024-2025")%>%group_by(Grade)%>%summarize(median_SGP=median(`SGP (Expectation=50)`))Growth_Sum_Math_Plot<-ggplot(Growth_Sum_Math, aes(x=`Grade`,fill =`Grade`, y =`median_SGP`, fill="skyblue")) +geom_bar(position="dodge", stat ="identity", fill="skyblue") +#facet_wrap(~Grade)+geom_text(aes( y =`median_SGP`, label =`median_SGP`,vjust =-.25))+theme_classic()+scale_fill_brewer(palette ="Blues")+theme(axis.title.x=element_blank(),axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="Grade Level",title ="Math Student Growth Percentile by Grade Level",caption ="Source: 2024-2025 Renaissance Star Math Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =1.05, y =53, label ="50th Percentile")
Code
Growth_Sum_Math_Plot
Code
Hist_Growth_Sum_Math_Plot<-HistGradeGrowthSum%>%filter(Subject =="Math")%>%filter(`Screening Window`=="Fall-Winter")%>%ggplot(aes(x=`Grade`,fill ="skyblue", y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity", fill ="skyblue") +#facet_wrap(~Grade)+geom_text(aes( y =`Median SGP`, label =`Median SGP`,vjust =-.25), position =position_dodge(width =1))+#theme_classic()+#scale_fill_brewer(palette = "Blues")+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="Grade Level",title ="Math Student Growth Percentile by Grade Level",caption ="Source: Renaissance Star Math Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =5.5, y =48, label ="50th Percentile")+facet_wrap(~Year)
Code
Hist_Growth_Sum_Math_Plot
RQ2 Grade 5 Math
Is there a relationship between Rising Tide’s Grade 5 Mathematics curriculum initiatives and Grade 5 student growth in mathematics?
Math_Gen_IEP_Achieve_Sum<-star_dem_math%>%filter(`Grade`=="5")%>%group_by(`School_Year`, Gender, IEP_Status)%>%summarize(`Median Test 1 PR`=median(`Test 1 PR`))Math_Gen_IEP_Achieve_Sum
Math Historical Growth
Code
# ggplot(aes(Species, fill=ifelse(Species=="setosa", "A","B"))) + # geom_bar(show.legend=FALSE) +# scale_fill_manual(values=c(A="blue", B="red"))Hist_Growth_Sum_G5Math_Plot<-HistGradeGrowthSum%>%filter(Subject =="Math")%>%filter(`Screening Window`=="Fall-Winter")%>%ggplot(aes(x=`Grade`,fill =ifelse(Grade =="5", "A", "B"), y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity", show.legend=FALSE) +scale_fill_manual(values =c(A="skyblue", B ="grey"))+#facet_wrap(~Grade)+geom_text(aes( y =`Median SGP`, label =`Median SGP`,vjust =-.25), position =position_dodge(width =1))+#theme_classic()+#scale_fill_brewer(palette = "Blues")+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="Grade Level",title ="Math Student Growth Percentile by Grade Level",caption ="Source: Renaissance Star Math Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =5.5, y =48, label ="50th Percentile")+facet_wrap(~Year)
Code
Hist_Growth_Sum_G5Math_Plot
Code
Hist_Growth_G5Math_Plot<-HistGradeGrowthSum%>%filter(Subject =="Math")%>%filter(Grade =="5")%>%filter(`Screening Window`=="Fall-Winter")%>%ggplot(aes(x=`Year`,fill ="skyblue", y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity", fill ="skyblue") +#facet_wrap(~Grade)+geom_text(aes( y =`Median SGP`, label =`Median SGP`, vjust =-.25), position =position_dodge(width =1))+theme_classic()+scale_fill_brewer(palette ="Paired")+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="School Year",title ="Improvement in Grade 5 Student Growth Percentile",caption ="Source: Renaissance Star Math Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =1., y =52, label ="Goal 50th Percentile")#+#theme_classic()#facet_wrap(~Year)
Code
Hist_Growth_G5Math_Plot
IEP
Code
IEP_Growth_G5Math_Plot<- Math_IEP_Sum%>%filter(Grade =="5")%>%ggplot(aes(x=`IEP`,fill =`School_Year`, y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`Median SGP`, x =`IEP`, label =`Median SGP`), position =position_dodge(width=.9),vjust =1)+theme_classic()+theme(#axis.title.x=element_blank(),#axis.text.x=element_blank(),# axis.text.x=element_text(angle =45, hjust = 1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+# facet_wrap(~`Screening Window`)+labs(y ="Median SGP",x="IEP Status",title ="Improved Growth For All Grade 5 Learners",caption ="Source: Renaissance Star Math Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =.70, y =52, label ="Goal 50th Percentile")#+
Code
IEP_Growth_G5Math_Plot
Code
Gen_Growth_G5Math_Plot<- Math_Gen_Sum%>%filter(Grade =="5")%>%ggplot(aes(x=`Gender`,fill =`School_Year`, y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`Median SGP`, x =`Gender`, label =`Median SGP`), position =position_dodge(width=.9),vjust =1)+theme_classic()+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),# axis.text.x=element_text(angle =45, hjust = 1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+# facet_wrap(~`Screening Window`)+labs(y ="Median SGP",x="Gender",title ="Improved Growth For All Grade 5 Learners",caption ="Source: Renaissance Star Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =.70, y =52, label ="Goal 50th Percentile")#+
Code
Gen_Growth_G5Math_Plot
RQ 3: ELA State Standards Mastery
Is there a relationship between the text type of a literacy standard (informational vs. literature) and Rising Tide High School students’ mastery of the standard?
ELA_Text_Summary%>%filter(`Screening Window`!="Spring")%>%ggplot(aes(x=`Text Type`,fill =`Text Type`, y =`Mean % Secure`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`Mean % Secure`, x =`Text Type`, label =`Mean % Secure`), position =position_dodge(width=.9),vjust =1)+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.text.x=element_text(angle =45, hjust =1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+facet_wrap(~`Screening Window`)+labs(y ="Mean % Secure",x="Domainl",title ="School State Standard Mastery by Text Type ELA",caption ="2024-2025 Star Reading Screening")
ELA_Text_Div_Summary%>%filter(`Screening Window`!="Spring")%>%ggplot(aes(x=`Text Type`,fill =`Screening Window`, y =`Mean % Secure`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`Mean % Secure`, x =`Text Type`, label =`Mean % Secure`), position =position_dodge(width=.9),vjust =1)+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.text.x=element_text(angle =45, hjust =1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+facet_wrap(~`Division`)+labs(y ="Mean % Secure",x="Domainl",title ="School State Standard Mastery by Text Type ELA",caption ="2024-2025 Star Reading Screening")
Code
ELA_Text_Domain_Summary%>%filter(`Screening Window`!="Spring")%>%ggplot(aes(x=`Domain`,fill =`Text Type`, y =`Mean % Secure`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`Mean % Secure`, x =`Domain`, label =`Mean % Secure`), position =position_dodge(width=.9),vjust =1)+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.text.x=element_text(angle =45, hjust =1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+facet_wrap(~`Screening Window`)+labs(y ="Mean % Secure",x="Domainl",title ="School State Standard Mastery by Text Type ELA",caption ="2024-2025 Star Reading Screening")
HS Informational Text Comparison
Code
ELA_Standards
Code
ELA_TextType_Sum_HS<-ELA_Standards%>%filter(`Division`=="HS")%>%filter (`Screening Window`!="Spring")%>%filter(`Reporting Category`=="Reading")%>%group_by(`Screening Window`, Grade, `Text Type`)%>%summarize(`Median % Secure`=round(median(`% Secure`))) ELA_TextType_Sum_HS%>%ggplot(aes(x=`Text Type`,fill =`Screening Window`, y =`Median % Secure`)) +geom_bar(position="dodge", stat ="identity") +#scale_x_reverse()+geom_text(aes( y =`Median % Secure`, label =`Median % Secure`,vjust =-.25), position =position_dodge(width =1)) +#theme_classic() +theme(axis.title.x=element_blank(),# axis.text.x=element_blank(),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+labs(y ="Median % Secure",x="Text Type",title ="HS Reading Standards Mastery by Text Type",caption =paste("Fall-Winter Screening", "2024-2025"))+facet_wrap(~Grade)
MS Informational Text Data Frames
Code
ELA_TextType_Sum_MS<-ELA_Standards%>%filter(`Division`=="MS")%>%filter (`Screening Window`!="Spring")%>%filter(`Reporting Category`=="Reading")%>%group_by(`Screening Window`, Grade, `Text Type`)%>%summarize(`Median % Secure`=round(median(`% Secure`))) ELA_TextType_Sum_MS%>%ggplot(aes(x=`Text Type`,fill =`Screening Window`, y =`Median % Secure`)) +geom_bar(position="dodge", stat ="identity") +#scale_x_reverse()+geom_text(aes( y =`Median % Secure`, label =`Median % Secure`,vjust =-.25), position =position_dodge(width =1)) +#theme_classic() +theme(axis.title.x=element_blank(),# axis.text.x=element_blank(),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+labs(y ="Median % Secure",x="Text Type",title ="MS Reading Standards Mastery by Text Type",caption =paste("Fall-Winter Screening", "2024-2025"))+facet_wrap(~Grade)
fit_ELA_text =lm(`% Secure`~ ( `Text Type`), data = HS_ELA_Standards)summary(fit_ELA_text)
Call:
lm(formula = `% Secure` ~ (`Text Type`), data = HS_ELA_Standards)
Residuals:
Min 1Q Median 3Q Max
-25.194 -9.352 -2.510 9.333 41.175
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.825 1.966 22.80 < 2e-16 ***
`Text Type`Literature 8.369 2.857 2.93 0.00451 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.43 on 74 degrees of freedom
(38 observations deleted due to missingness)
Multiple R-squared: 0.1039, Adjusted R-squared: 0.09183
F-statistic: 8.584 on 1 and 74 DF, p-value: 0.004507
Code
fit_ELA_text_domain =lm(`% Secure`~ ( `Text Type`+`Screening Window`+`Domain`), data = HS_ELA_Standards)summary(fit_ELA_text_domain)
Call:
lm(formula = `% Secure` ~ (`Text Type` + `Screening Window` +
Domain), data = HS_ELA_Standards)
Residuals:
Min 1Q Median 3Q Max
-28.378 -8.155 -1.182 7.971 40.030
Coefficients:
Estimate Std. Error t value
(Intercept) 39.314 3.175 12.382
`Text Type`Literature 8.840 2.806 3.151
`Screening Window`Winter 4.947 2.791 1.773
DomainIntegration of Knowledge and Ideas 7.276 3.694 1.970
DomainKey Ideas and Details 1.708 3.512 0.486
DomainRange of Reading and Level of Text Complexity 3.417 4.967 0.688
Pr(>|t|)
(Intercept) <2e-16 ***
`Text Type`Literature 0.0024 **
`Screening Window`Winter 0.0806 .
DomainIntegration of Knowledge and Ideas 0.0528 .
DomainKey Ideas and Details 0.6282
DomainRange of Reading and Level of Text Complexity 0.4938
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.17 on 70 degrees of freedom
(38 observations deleted due to missingness)
Multiple R-squared: 0.1886, Adjusted R-squared: 0.1307
F-statistic: 3.255 on 5 and 70 DF, p-value: 0.01063
Code
HS_ELA_Text_Facet <- HS_ELA_Standards %>%ggplot( aes(x=`% Secure`, fill=`Text Type`)) +geom_histogram( color="#e9ecef", alpha=0.6,binwidth=15, position ='identity') +scale_fill_manual(values=c("#69b3a2", "#404080")) +theme_classic() +labs( y ="",title ="High School State Standard Mastery Distribution",x ="% Students Secure",caption ="Star Reading: State Standards Mastery")+facet_wrap(~`Text Type`)HS_ELA_Text_Facet
Scaling?
Code
stargazer(fit_ELA_text_domain, type ="text")
===============================================================================
Dependent variable:
---------------------------
`% Secure`
-------------------------------------------------------------------------------
`Text Type`Literature 8.840***
(2.806)
`Screening Window`Winter 4.947*
(2.791)
DomainIntegration of Knowledge and Ideas 7.276*
(3.694)
DomainKey Ideas and Details 1.708
(3.512)
DomainRange of Reading and Level of Text Complexity 3.417
(4.967)
Constant 39.314***
(3.175)
-------------------------------------------------------------------------------
Observations 76
R2 0.189
Adjusted R2 0.131
Residual Std. Error 12.166 (df = 70)
F Statistic 3.255** (df = 5; 70)
===============================================================================
Note: *p<0.1; **p<0.05; ***p<0.01
Code
# # variable_labels <- c(# "% Secure", # For the dependent variable# "Text Type: Informational", # For the reference category# "Text Type: Literature", # For the first other category# "Screening Window: Fall", # For the second other category# "Screening Window: Winter",# "Domain: Craft and Structure",# "Domain: Integration of Knolwedge and Ideas",# "Domain: Key Ideas and Details",# "Domain: Range of Reading and Level of Text Complexity"# )# stargazer(fit_ELA_text_domain, covariate.labels = variable_labels, type = "text")
Source Code
---title: "DACSS 604 Final Project"author: "Theresa Szczepanski"format: html: df-print: paged toc: true code-fold: true code-copy: true code-tools: true embed-resources: true self-contained-math: trueeditor: markdown: wrap: 72---# Load Libraries```{r}#| label: setup#| warning: false#| message: false# load the needed librarieslibrary(tidyverse)library(ggplot2)library(lubridate)library(readxl)library(hrbrthemes)library(viridis)library(ggpubr)library(purrr)library(plotly)library(dplyr)library(RColorBrewer)library(stargazer)knitr::opts_chunk$set(echo =TRUE, warning=FALSE, message=FALSE)#source('readin_lib.R')#source('student_perf_lib.R')```# Load the Data## State Standards Mastery Summary```{r}ELA_Standards<-read_csv("data_604/State_Standards-_Mastery_Star_2425_ELA.csv")%>%mutate(Division =case_when( Grade ==9| Grade ==10~"HS", Grade ==5| Grade ==6| Grade ==7| Grade ==8~"MS" ))%>%mutate(Grade =as.factor(Grade))%>%mutate(`Reporting Category`=as.factor(`Reporting Category`))%>%mutate(`Domain`=as.factor(`Domain`))%>%mutate(`Division`=as.factor(`Division`))%>%mutate(`Screening Window`=as.factor(`Screening Window`))%>%mutate(`Text Type`=as.factor(`Text Type`))ELA_Standards```## Growth/Demographic Summary```{r}star_dem_all_private<-read_csv("data_604/student_star_dem_all_private.csv")%>%mutate(`Grade`=as.factor(`Grade`))%>%mutate(`Grade`=recode_factor(`Grade`,"5"="5","6"="6","7"="7","8"="8","9"="9","10"="10",.ordered =TRUE))%>%mutate(School_Year =as.factor(School_Year))%>%mutate(School_Year =recode_factor(School_Year,"2023-2024"="2023-2024","2024-2025"="2024-2025",.ordered =TRUE))%>%mutate(`Assignment Type`=as.factor(`Assignment Type`))%>%mutate(`Test 1 Benchmark Category`=recode_factor(`Test 1 Benchmark Category`,"At/Above Benchmark"="At/Above Benchmark","On Watch"="On Watch","Intervention"="Intervention","Urgent Intervention"="Urgent Intervention",.ordered =TRUE))%>%mutate(IEP_Status =as.factor(IEP_Status))%>%mutate(`504_Status`=as.factor(`504_Status`))%>%mutate(`Low_Income_Status`=as.factor(Low_Income_Status))%>%mutate(`Gender`=as.factor(Gender) )%>%mutate(`Gender_Status`=case_when( Gender =="Male"~0, Gender =="Female"~1 ))star_dem_all_private```## Historical Summary Data Frames```{r}HistSchoolBenchSum<-read_csv("data_604/StarBenchmarkSummary_School.csv")%>%mutate(Year =as.factor(Year))%>%mutate(`Screening Window`=as.factor(`Screening Window`))%>%mutate(`Benchmark_Status`=recode_factor(`Benchmark_Status`,"At/Above Benchmark"="At/Above Benchmark","Below Benchmark"="Below Benchmark",.ordered =TRUE))%>%select(Year, `Screening Window`, Subject, `Benchmark_Status`, `% Students`)HistSchoolBenchSumHistGradeGrowthSum<-read_csv("data_604/StarBenchmarkSummary_SGP_Sum.csv")%>%mutate(Year =as.factor(Year))%>%mutate(`Screening Window`=as.factor(`Screening Window`))%>%mutate(`Subject`=as.factor(`Subject`))%>%mutate(`Grade`=as.factor(Grade))%>%mutate(`Grade`=recode_factor(`Grade`,"Grade 5"="Grade 5","Grade 6"="Grade 6","Grade 7"="Grade 7","Grade 8"="Grade 8","Grade 9"="Grade 9","Grade 10"="Grade 10",.ordered =TRUE))%>%select(Year, `Screening Window`, `Grade`, Subject, `Median SGP`)HistGradeGrowthSum```# RQ1:Are Rising Tide students making progress toward our achievementaccountability goal: “80% of Rising Students will test as at or aboveBenchmark in Reading on the Renaissance Star Reading and StarMathematics screening assessments.?## Historical Benchmark_Status```{r}star_dem_all_private<-star_dem_all_private%>%mutate(Benchmark_Status =case_when(`Test 1 PR`>=40~"At/Above Benchmark",`Test 1 PR`<40~"Below Benchmark"))%>%mutate(Benchmark_Status =as.factor(Benchmark_Status))```### Statistical Test#### Acheivement: Math```{r}star_dem_math<- star_dem_all_private%>%filter(`Assignment Type`=="Star Math")fit_Math_achieve =lm(`Test 1 PR`~ School_Year +`IEP_Status`, data = star_dem_math)summary(fit_Math_achieve)#star_dem_math```#### Growth: Math```{r}fit_Math_growth =lm(`SGP (Expectation=50)`~`Test 1 PR`+ Gender +`IEP_Status`+ Grade, data = star_dem_math)summary(fit_Math_growth)```#### Acheivement: ELA```{r}star_dem_ELA<- star_dem_all_private%>%filter(`Assignment Type`=="Star Reading")fit_ELA_achieve =lm(`Test 1 PR`~ School_Year +`IEP_Status`, data = star_dem_ELA)summary(fit_ELA_achieve)```#### Growth: ELA```{r}fit_ELA_growth =lm(`SGP (Expectation=50)`~`IEP_Status`+ School_Year + Grade, data = star_dem_ELA)summary(fit_ELA_growth)``````{r}Bench_Sum_Plot<-HistSchoolBenchSum%>%filter(`Year`=="2024-2025"&`Screening Window`=="Winter"|`Year`=="2023-2024"&`Screening Window`=="Spring")%>%filter(`Benchmark_Status`=="At/Above Benchmark")%>%ggplot(aes(x=`Benchmark_Status`,fill =`Year`, y =`% Students`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`% Students`, label =`% Students`,vjust =-.25), position =position_dodge(width =1)) +#theme_classic() +theme(axis.title.x=element_blank(),# axis.text.x = element_text(angle = 45, , hjust = 1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+labs(y ="% Students",x="Benchmark Category",title ="Rising Tide Benchmark Achievement by School Year",caption ="Source: Renaissance Star Literacy and Math Screening")+geom_hline(yintercept =80, color ="grey")+annotate("text", x = .75, y =85, label ="80% Goal") +facet_wrap(~Subject)``````{r}#| eval: false#| fig-cap: "Rising Tide Benchmark Achievement by School Year, Renaissance Star Literacy and Math"#| fig-alt: >#| A bar blot with school year (ordered, factor) on the x-axis and the percentage of students#| classified as At/Above benchmark on the y-axis, faceted by subject (English Language Arts or Math)#| Plot illustrates the relationship between the three variables.Bench_Sum_Plot```# Subject Level Fall Winter Growth Comparison## ELA Growth```{r}Growth_Sum_ELA<-star_dem_all_private%>%filter(`School_Year`=="2024-2025")%>%filter(`Assignment Type`=="Star Reading")%>%group_by(Grade)%>%summarize(median_SGP=median(`SGP (Expectation=50)`))Growth_Sum_ELA_Plot<-ggplot(Growth_Sum_ELA, aes(x=`Grade`, y =`median_SGP`,fill="skyblue")) +geom_bar(position="dodge", stat ="identity", fill="skyblue") +#facet_wrap(~Grade)+geom_text(aes( y =`median_SGP`, label =`median_SGP`,vjust =-.25))+theme_classic()+#scale_color_brewer(palette = "Blues")+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="Grade Level",title ="Literacy Student Growth Percentile by Grade Level",caption ="Source: 2024-2025 Renaissance Star Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =1., y =52, label ="50th Percentile")``````{r}#| eval: false#| fig-cap: "Rising Tide Literacy Growth by Grade Level, 2024-2025 Renaissance Star Literacy Screening"#| fig-alt: >#| A bar blot with Grade level (ordered, factor) on the x-axis and the median student growth percentile #| (SGP) in literacy on the y-axis#| Plot illustrates the relationship between the two variables.Growth_Sum_ELA_Plot```### ELA Historical Growth```{r}Hist_Growth_Sum_ELA_Plot<-HistGradeGrowthSum%>%filter(Subject =="ELA")%>%filter(`Screening Window`=="Fall-Winter")%>%ggplot(aes(x=`Grade`,fill ="skyblue", y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity", fill ="skyblue") +#facet_wrap(~Grade)+geom_text(aes( y =`Median SGP`, label =`Median SGP`,vjust =-.25), position =position_dodge(width =1))+#theme_classic()+#scale_fill_brewer(palette = "Blues")+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="Grade Level",title ="Literacy Student Growth Percentile by Grade Level",caption ="Source: Renaissance Star Literacy Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =1.55, y =52, label ="50th Percentile")+facet_wrap(~Year)``````{r}#| eval: false#| fig-cap: "Rising Tide Literacy Growth by Grade Level, school year 2024-2025 and 2023-2024, Renaissance Star Literacy Screening"#| #| fig-alt: >#| A bar blot with Grade level (ordered, factor) on the x-axis and the median student growth percentile #| (SGP) in literacy on the y-axis, faceted by school year (ordered, factor).#| Plot illustrates the relationship between the three variables.#| Hist_Growth_Sum_ELA_Plot```## Math Growth```{r}Growth_Sum_Math<-star_dem_all_private%>%filter(`Assignment Type`=="Star Math")%>%filter(`School_Year`=="2024-2025")%>%group_by(Grade)%>%summarize(median_SGP=median(`SGP (Expectation=50)`))Growth_Sum_Math_Plot<-ggplot(Growth_Sum_Math, aes(x=`Grade`,fill =`Grade`, y =`median_SGP`, fill="skyblue")) +geom_bar(position="dodge", stat ="identity", fill="skyblue") +#facet_wrap(~Grade)+geom_text(aes( y =`median_SGP`, label =`median_SGP`,vjust =-.25))+theme_classic()+scale_fill_brewer(palette ="Blues")+theme(axis.title.x=element_blank(),axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="Grade Level",title ="Math Student Growth Percentile by Grade Level",caption ="Source: 2024-2025 Renaissance Star Math Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =1.05, y =53, label ="50th Percentile")``````{r}#| eval: false#| fig-cap: "Rising Tide Math Growth by Grade Level, 2024-2025 Renaissance Star Literacy Screening"#| fig-alt: >#| A bar blot with Grade level (ordered, factor) on the x-axis and the median student growth percentile #| (SGP) in math on the y-axis#| Plot illustrates the relationship between the two variables.Growth_Sum_Math_Plot``````{r}Hist_Growth_Sum_Math_Plot<-HistGradeGrowthSum%>%filter(Subject =="Math")%>%filter(`Screening Window`=="Fall-Winter")%>%ggplot(aes(x=`Grade`,fill ="skyblue", y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity", fill ="skyblue") +#facet_wrap(~Grade)+geom_text(aes( y =`Median SGP`, label =`Median SGP`,vjust =-.25), position =position_dodge(width =1))+#theme_classic()+#scale_fill_brewer(palette = "Blues")+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="Grade Level",title ="Math Student Growth Percentile by Grade Level",caption ="Source: Renaissance Star Math Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =5.5, y =48, label ="50th Percentile")+facet_wrap(~Year)``````{r}#| eval: false#| fig-cap: "Rising Tide Math Growth by Grade Level, school year 2024-2025 and 2023-2024, Renaissance Star Math Screening"#| #| fig-alt: >#| A bar blot with Grade level (ordered, factor) on the x-axis and the median student growth percentile #| (SGP) in math on the y-axis, faceted by school year (ordered, factor).#| Plot illustrates the relationship between the three variables.#| Hist_Growth_Sum_Math_Plot```# RQ2 Grade 5 MathIs there a relationship between Rising Tide’s Grade 5 Mathematicscurriculum initiatives and Grade 5 student growth in mathematics?```{r}Math_IEP_Sum<-star_dem_math%>%mutate(IEP =case_when(IEP_Status ==1~"Yes", IEP_Status ==0~"No"))%>%filter(`Assignment Type`=="Star Math")%>%group_by(Grade, `School_Year`, IEP)%>%summarize(`Median SGP`=median(`SGP (Expectation=50)`))Math_IEP_SumMath_Gen_Sum<-star_dem_math%>%filter(`Assignment Type`=="Star Math")%>%group_by(Grade, `School_Year`, Gender)%>%summarize(`Median SGP`=median(`SGP (Expectation=50)`))Math_Gen_Sumstar_dem_mathMath_Gen_IEP_Sum<-star_dem_math%>%filter(`Grade`=="5")%>%group_by(`School_Year`, Gender, IEP_Status)%>%summarize(`Median SGP`=median(`SGP (Expectation=50)`))Math_Gen_IEP_SumMath_Gen_IEP_Achieve_Sum<-star_dem_math%>%filter(`Grade`=="5")%>%group_by(`School_Year`, Gender, IEP_Status)%>%summarize(`Median Test 1 PR`=median(`Test 1 PR`))Math_Gen_IEP_Achieve_Sum```### Math Historical Growth```{r}# ggplot(aes(Species, fill=ifelse(Species=="setosa", "A","B"))) + # geom_bar(show.legend=FALSE) +# scale_fill_manual(values=c(A="blue", B="red"))Hist_Growth_Sum_G5Math_Plot<-HistGradeGrowthSum%>%filter(Subject =="Math")%>%filter(`Screening Window`=="Fall-Winter")%>%ggplot(aes(x=`Grade`,fill =ifelse(Grade =="5", "A", "B"), y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity", show.legend=FALSE) +scale_fill_manual(values =c(A="skyblue", B ="grey"))+#facet_wrap(~Grade)+geom_text(aes( y =`Median SGP`, label =`Median SGP`,vjust =-.25), position =position_dodge(width =1))+#theme_classic()+#scale_fill_brewer(palette = "Blues")+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="Grade Level",title ="Math Student Growth Percentile by Grade Level",caption ="Source: Renaissance Star Math Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =5.5, y =48, label ="50th Percentile")+facet_wrap(~Year)``````{r}#| eval: false#| fig-cap: "Rising Tide Math Growth by Grade Level, school year 2024-2025 and 2023-2024, Renaissance Star Math Screening"#| #| fig-alt: >#| A bar blot with Grade level (ordered, factor) on the x-axis and the median student growth percentile #| (SGP) in math on the y-axis, faceted by school year (ordered, factor).#| Plot illustrates the relationship between the three variables.#| Hist_Growth_Sum_G5Math_Plot``````{r}Hist_Growth_G5Math_Plot<-HistGradeGrowthSum%>%filter(Subject =="Math")%>%filter(Grade =="5")%>%filter(`Screening Window`=="Fall-Winter")%>%ggplot(aes(x=`Year`,fill ="skyblue", y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity", fill ="skyblue") +#facet_wrap(~Grade)+geom_text(aes( y =`Median SGP`, label =`Median SGP`, vjust =-.25), position =position_dodge(width =1))+theme_classic()+scale_fill_brewer(palette ="Paired")+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.ticks.x=element_blank())+labs(y ="Median SGP",x="School Year",title ="Improvement in Grade 5 Student Growth Percentile",caption ="Source: Renaissance Star Math Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =1., y =52, label ="Goal 50th Percentile")#+#theme_classic()#facet_wrap(~Year)``````{r}#| eval: false#| fig-cap: "Rising Tide Grade 5 Math Growth by school year 2024-2025 and 2023-2024, Renaissance Star Math Screening"#| #| fig-alt: >#| A bar blot with school year (ordered, factor) on the x-axis and the median G5 student growth #| percentile (SGP) in math on the y-axis.#| Plot illustrates the relationship between the two variables.#| Hist_Growth_G5Math_Plot```## IEP```{r} IEP_Growth_G5Math_Plot<- Math_IEP_Sum%>%filter(Grade =="5")%>%ggplot(aes(x=`IEP`,fill =`School_Year`, y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`Median SGP`, x =`IEP`, label =`Median SGP`), position =position_dodge(width=.9),vjust =1)+theme_classic()+theme(#axis.title.x=element_blank(),#axis.text.x=element_blank(),# axis.text.x=element_text(angle =45, hjust = 1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+# facet_wrap(~`Screening Window`)+labs(y ="Median SGP",x="IEP Status",title ="Improved Growth For All Grade 5 Learners",caption ="Source: Renaissance Star Math Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =.70, y =52, label ="Goal 50th Percentile")#+``````{r}#| eval: false#| fig-cap: "Rising Tide Grade 5 Math Growth by disability status (IEP) and school year 2024-2025 and 2023-2024, Renaissance Star Math Screening"#| #| fig-alt: >#| A clustered, bar blot with IEP status (ordered, factor) on the x-axis and the #| median G5 student growthpercentile (SGP) in math on the y-axis.#| Bars are colored by the school year (ordered, factor)#| Plot illustrates the relationship between the three variables.#| IEP_Growth_G5Math_Plot``````{r}Gen_Growth_G5Math_Plot<- Math_Gen_Sum%>%filter(Grade =="5")%>%ggplot(aes(x=`Gender`,fill =`School_Year`, y =`Median SGP`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`Median SGP`, x =`Gender`, label =`Median SGP`), position =position_dodge(width=.9),vjust =1)+theme_classic()+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),# axis.text.x=element_text(angle =45, hjust = 1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+# facet_wrap(~`Screening Window`)+labs(y ="Median SGP",x="Gender",title ="Improved Growth For All Grade 5 Learners",caption ="Source: Renaissance Star Screening")+geom_hline(yintercept =50, color ="grey")+annotate("text", x =.70, y =52, label ="Goal 50th Percentile")#+``````{r}#| eval: false#| fig-cap: "Rising Tide Grade 5 Math Growth by disability status (IEP) and school year 2024-2025 and 2023-2024, Renaissance Star Math Screening"#| #| fig-alt: >#| A clustered, bar blot with Gender (ordered, factor) on the x-axis and the #| median G5 student growthpercentile (SGP) in math on the y-axis.#| Bars are colored by the school year (ordered, factor)#| Plot illustrates the relationship between the three variables.#| Gen_Growth_G5Math_Plot```# RQ 3: ELA State Standards MasteryIs there a relationship between the text type of a literacy standard(informational vs. literature) and Rising Tide High School students’mastery of the standard?## School Informational Text by Domain```{r}ELA_StandardsELA_Text_Summary<-ELA_Standards%>%filter(`Reporting Category`=="Reading")%>%group_by(`Text Type`, `Screening Window`)%>%summarize(`Mean % Secure`=round(mean(`% Secure`)),`Median % Secure`=round(median(`% Secure`)))ELA_Text_SummaryELA_Text_Summary%>%filter(`Screening Window`!="Spring")%>%ggplot(aes(x=`Text Type`,fill =`Text Type`, y =`Mean % Secure`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`Mean % Secure`, x =`Text Type`, label =`Mean % Secure`), position =position_dodge(width=.9),vjust =1)+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.text.x=element_text(angle =45, hjust =1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+facet_wrap(~`Screening Window`)+labs(y ="Mean % Secure",x="Domainl",title ="School State Standard Mastery by Text Type ELA",caption ="2024-2025 Star Reading Screening")ELA_Text_Domain_Summary<-ELA_Standards%>%filter(`Reporting Category`=="Reading")%>%group_by(`Text Type`, `Domain`, `Screening Window`)%>%summarize(`Mean % Secure`=round(mean(`% Secure`)),`Median % Secure`=round(median(`% Secure`)))ELA_Text_Domain_SummaryELA_Text_Div_Summary<-ELA_Standards%>%filter(`Reporting Category`=="Reading")%>%group_by(`Text Type`, `Division`, `Screening Window`)%>%summarize(`Mean % Secure`=round(mean(`% Secure`)),`Median % Secure`=round(median(`% Secure`)))ELA_Text_Div_SummaryELA_Text_Div_Summary%>%filter(`Screening Window`!="Spring")%>%ggplot(aes(x=`Text Type`,fill =`Screening Window`, y =`Mean % Secure`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`Mean % Secure`, x =`Text Type`, label =`Mean % Secure`), position =position_dodge(width=.9),vjust =1)+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.text.x=element_text(angle =45, hjust =1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+facet_wrap(~`Division`)+labs(y ="Mean % Secure",x="Domainl",title ="School State Standard Mastery by Text Type ELA",caption ="2024-2025 Star Reading Screening")ELA_Text_Domain_Summary%>%filter(`Screening Window`!="Spring")%>%ggplot(aes(x=`Domain`,fill =`Text Type`, y =`Mean % Secure`)) +geom_bar(position="dodge", stat ="identity") +geom_text(aes( y =`Mean % Secure`, x =`Domain`, label =`Mean % Secure`), position =position_dodge(width=.9),vjust =1)+theme(axis.title.x=element_blank(),#axis.text.x=element_blank(),axis.text.x=element_text(angle =45, hjust =1),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+facet_wrap(~`Screening Window`)+labs(y ="Mean % Secure",x="Domainl",title ="School State Standard Mastery by Text Type ELA",caption ="2024-2025 Star Reading Screening")```## HS Informational Text Comparison```{r}ELA_StandardsELA_TextType_Sum_HS<-ELA_Standards%>%filter(`Division`=="HS")%>%filter (`Screening Window`!="Spring")%>%filter(`Reporting Category`=="Reading")%>%group_by(`Screening Window`, Grade, `Text Type`)%>%summarize(`Median % Secure`=round(median(`% Secure`))) ELA_TextType_Sum_HS%>%ggplot(aes(x=`Text Type`,fill =`Screening Window`, y =`Median % Secure`)) +geom_bar(position="dodge", stat ="identity") +#scale_x_reverse()+geom_text(aes( y =`Median % Secure`, label =`Median % Secure`,vjust =-.25), position =position_dodge(width =1)) +#theme_classic() +theme(axis.title.x=element_blank(),# axis.text.x=element_blank(),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+labs(y ="Median % Secure",x="Text Type",title ="HS Reading Standards Mastery by Text Type",caption =paste("Fall-Winter Screening", "2024-2025"))+facet_wrap(~Grade)```## MS Informational Text Data Frames```{r}ELA_TextType_Sum_MS<-ELA_Standards%>%filter(`Division`=="MS")%>%filter (`Screening Window`!="Spring")%>%filter(`Reporting Category`=="Reading")%>%group_by(`Screening Window`, Grade, `Text Type`)%>%summarize(`Median % Secure`=round(median(`% Secure`))) ELA_TextType_Sum_MS%>%ggplot(aes(x=`Text Type`,fill =`Screening Window`, y =`Median % Secure`)) +geom_bar(position="dodge", stat ="identity") +#scale_x_reverse()+geom_text(aes( y =`Median % Secure`, label =`Median % Secure`,vjust =-.25), position =position_dodge(width =1)) +#theme_classic() +theme(axis.title.x=element_blank(),# axis.text.x=element_blank(),axis.ticks.x=element_blank())+scale_fill_brewer(palette ="Paired")+labs(y ="Median % Secure",x="Text Type",title ="MS Reading Standards Mastery by Text Type",caption =paste("Fall-Winter Screening", "2024-2025"))+facet_wrap(~Grade)```# Statistical Tests## G5 Growth Distribution```{r}MathHistGrowth<- star_dem_mathG5MathHistGrowth<-star_dem_math%>%filter(Grade =="5")G5_Hist_Year <- G5MathHistGrowth %>%ggplot( aes(x=`SGP (Expectation=50)`, fill=School_Year)) +geom_histogram( color="#e9ecef", alpha=0.6,binwidth=10, position ='identity') +scale_fill_manual(values=c("#69b3a2", "#404080")) +theme_classic() +labs(fill="")#+# facet_wrap(~Year)G5_Hist_YearG5_Hist_Year_Facet <- G5MathHistGrowth %>%ggplot( aes(x=`SGP (Expectation=50)`, fill=School_Year)) +geom_histogram( color="#e9ecef", alpha=0.6,binwidth=15, position ='identity') +scale_fill_manual(values=c("#69b3a2", "#404080")) +theme_classic() +labs(fill="")+labs( y ="",title ="Grade 5 Student Growth Distribution",x ="Student Growth Percentile (SGP)",caption ="Star Math Assessments")+facet_wrap(~School_Year)G5_Hist_Year_FacetGrowth_Hist<-MathHistGrowth%>%ggplot( aes(x=`SGP (Expectation=50)`)) +#geom_histogram(alpha=0.6, binwidth = 15) +#ggplot(aes()+geom_histogram(alpha = .9, binwidth =15)+#scale_fill_viridis(discrete=TRUE) +#scale_color_viridis(discrete=TRUE) +theme_classic()Growth_Hist```## Growth Summary```{r}#summary(MathHistGrowth)G5MathHistGrowthfit_G5Math_growth =lm(`SGP (Expectation=50)`~ School_Year +`Test 1 PR`, data = G5MathHistGrowth)#summary(fit_G5Math_growth)AIC(fit_G5Math_growth)BIC(fit_G5Math_growth)fit_Math_growth =lm(`SGP (Expectation=50)`~ Grade + School_Year +`Test 1 PR`+ Grade*School_Year, data = MathHistGrowth)#summary(fit_Math_growth)#summary(MathHistGrowth)MathHistGrowth%>%summarize(`Mean SGP`=mean(`SGP (Expectation=50)`),`SD SGP`=sd(`SGP (Expectation=50)`))MathHistGrowth%>%group_by(Grade)%>%summarize(`Mean SGP`=mean(`SGP (Expectation=50)`),`SD SGP`=sd(`SGP (Expectation=50)`))summary(fit_G5Math_growth)``````{r}stargazer(fit_G5Math_growth, type ="text") ```### Diagnosing Model Fit```{r}plot(fit_G5Math_growth, which =1:6)cooks=4/179cooks #.0223```### Remove Outliers```{r}G5MathHistGrowth```### Scaled Math Model```{r}G5MathHistGrowth_Scaled<-G5MathHistGrowth%>%mutate(across(where(is.numeric), scale))G5MathHistGrowth_Scaled``````{r}fit_G5Math_growth_scaled =lm(`SGP (Expectation=50)`~ School_Year +`Test 1 PR`, data = G5MathHistGrowth_Scaled)summary(fit_G5Math_growth_scaled)AIC(fit_G5Math_growth_scaled)BIC(fit_G5Math_growth_scaled)``````{r}stargazer(fit_G5Math_growth_scaled, type ="text") ```##ELA Standards```{r}ELA_StandardsHS_ELA_Standards<-ELA_Standards%>%filter(Grade ==9|Grade ==10)%>%filter(`Text Type`=="Informational Text"|`Text Type`=="Literature")HS_ELA_Standardsfit_ELA_text =lm(`% Secure`~ ( `Text Type`), data = HS_ELA_Standards)summary(fit_ELA_text)fit_ELA_text_domain =lm(`% Secure`~ ( `Text Type`+`Screening Window`+`Domain`), data = HS_ELA_Standards)summary(fit_ELA_text_domain)HS_ELA_Text_Facet <- HS_ELA_Standards %>%ggplot( aes(x=`% Secure`, fill=`Text Type`)) +geom_histogram( color="#e9ecef", alpha=0.6,binwidth=15, position ='identity') +scale_fill_manual(values=c("#69b3a2", "#404080")) +theme_classic() +labs( y ="",title ="High School State Standard Mastery Distribution",x ="% Students Secure",caption ="Star Reading: State Standards Mastery")+facet_wrap(~`Text Type`)HS_ELA_Text_Facet```# Scaling?```{r}stargazer(fit_ELA_text_domain, type ="text")# # variable_labels <- c(# "% Secure", # For the dependent variable# "Text Type: Informational", # For the reference category# "Text Type: Literature", # For the first other category# "Screening Window: Fall", # For the second other category# "Screening Window: Winter",# "Domain: Craft and Structure",# "Domain: Integration of Knolwedge and Ideas",# "Domain: Key Ideas and Details",# "Domain: Range of Reading and Level of Text Complexity"# )# stargazer(fit_ELA_text_domain, covariate.labels = variable_labels, type = "text")```